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| Titel |
Probabilistic modelling of rainfall induced landslide hazard assessment |
| VerfasserIn |
S. Kawagoe, S. Kazama, P. R. Sarukkalige |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1027-5606
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| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 14, no. 6 ; Nr. 14, no. 6 (2010-06-25), S.1047-1061 |
| Datensatznummer |
250012342
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| Publikation (Nr.) |
 copernicus.org/hess-14-1047-2010.pdf |
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| Zusammenfassung |
| To evaluate the frequency and distribution of landslides hazards over Japan,
this study uses a probabilistic model based on multiple logistic regression
analysis. Study particular concerns several important physical parameters
such as hydraulic parameters, geographical parameters and the geological
parameters which are considered to be influential in the occurrence of
landslides. Sensitivity analysis confirmed that hydrological parameter
(hydraulic gradient) is the most influential factor in the occurrence of
landslides. Therefore, the hydraulic gradient is used as the main hydraulic
parameter; dynamic factor which includes the effect of heavy rainfall and
their return period. Using the constructed spatial data-sets, a multiple
logistic regression model is applied and landslide hazard probability maps
are produced showing the spatial-temporal distribution of landslide hazard
probability over Japan. To represent the landslide hazard in different
temporal scales, extreme precipitation in 5 years, 30 years, and 100 years
return periods are used for the evaluation. The results show that the
highest landslide hazard probability exists in the mountain ranges on the
western side of Japan (Japan Sea side), including the Hida and Kiso, Iide
and the Asahi mountainous range, the south side of Chugoku mountainous
range, the south side of Kyusu mountainous and the Dewa mountainous range
and the Hokuriku region. The developed landslide hazard probability maps in
this study will assist authorities, policy makers and decision makers, who
are responsible for infrastructural planning and development, as they can
identify landslide-susceptible areas and thus decrease landslide damage
through proper preparation. |
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